Standard of care neurological monitoring for patients with severe traumatic brain injury (TBI) - a leading cause of death and long-term neurological impairment in children and adults-has not changed in decades, relying mainly on intracranial pressure {ICP) monitoring. Remarkably, the use of ICP alone as a therapeutic target for severe TBI is currently controversial due to a lack of robust supporting evidence, especially for its use in children. To address this clinical need, we developed the ICP-PC02 Compliance Index (ICP-PCI), an algorithm to compute dynamic intracranial compliance in real-time by integrating continuous ICP and end tidal CO, (ETC02) data streams. Bedside assessment of intracranial compliance-the relationship between changes in ICP and concomitant changes in intracranial volume, has been limited because of the lack of point-of-care devices to measure cerebral blood flow (CBF)/cerebral blood volume (CBV). The ICP-PCI is based on the well-known and robust relationship between the partial pressure of CO2 in blood (PC02) and CBV, where a change in PC02 of 1 mmHg induces an -3% change in CBF in patients with severe TBI. Since CBF is proportional to blood vessel radius to the fourth power, changes in CBF reflect immediate changes in CBV. As continuous ICP and ETC02 monitoring are standard of care for patients with severe TBI, ICP-PCI can be determined using existing ICU monitoring. To date we have obtained preliminary data in children with severe TBI in an IRB approved study that validates the physiologic premise and demonstrates feasibility for measurement of ICPPCI using existing, continuous ICU monitoring deemed guidelines-based standard of care. In this proposal, dense time series data, including continuous ETC02, ICP, and other physiologic waveforms will be interrogated. ICP-PCI will be calculated as the running moment-to-moment correlation between ETC02 and ICP across optimized temporal epochs, and subject to additional signal processing. We will confirm our findings across a larger cohort and define the temporal pattern of ICP-PCI and associations with relevant clinical variables: ICP, CPP, duration of ICP monitoring, medical and surgical interventions, and ICU and hospital length of stay. In addition, high-density, time series data will be integrated and time-synchronized with electronic health record (EHR) data and simulation models will be generated and refined to define the capacity for ICP-PCI to predict the need and response to relevant medical and surgical interventions. Clinical application of ICP-PCI will be compared head-to-head with ICP alone. Successful validation of ICP-PCI would lay the groundwork for the development of a valuable clinical tool for all Centers managing children and possibly adults with severe TBI, that could be readily integrated and implemented using existing ICU monitoring.